Data Engineer

Through proprietary software and AI, along with a focus on customer delight, Sleek makes the back-office easy for micro SMEs.We give Entrepreneurs time back to focus on what they love doing - growing their business and being with customers. With a surging number of Entrepreneurs globally, we are innovating in a highly lucrative space.We operate 3 business segments:Corporate Secretary: Automating the company incorporation, secretarial, filing, Nominee Director, mailroom and immigration processes via custom online robots and SleekSign. We are the market leaders in Singapore with ~5% market share of all new business incorporationsAccounting & Bookkeeping: Redefining what it means to do Accounting, Bookkeeping, Tax and Payroll thanks to our proprietary SleekBooks ledger, AI tools and exceptional customer serviceFinTech payments: Overcoming a key challenge for Entrepreneurs by offering digital banking services to new businessesSleek launched in 2017 and now has around 15,000 customers across our offices in Singapore, Hong Kong, Australia and the UK. We have around 500 staff with an intact startup mindset. We have recently raised Series B financing off the back of >70% compound annual growth in Revenue over the last 5 years. Sleek has been recognised by The Financial Times, The Straits Times, Forbes and LinkedIn as one of the fastest growing companies in Asia. Backed by world-class investors, we are on track to be one of the few cash flow positive, tech-enabled unicorns based out of Asia Pacific. RequirementsWe are looking for a skilled and passionate Data Engineer to join our growing Data Platform team. Mission: You will design, build, and maintain robust data pipelines on databricks and AWS infrastructure that power analytics and reporting capabilities across the organization.Key responsibilities:Design and implement scalable ETL/ELT pipelines using both batch and streaming patterns – Build and maintain ingestion workflows from diverse sources (databases, APIs, event streams) –Implement Change Data Capture (CDC), full-load, and incremental ingestion strategies. Develop and manage data workflows using Apache Airflow for orchestration.Configure and manage data ingestion connectors using Airbyte.Work with Databricks to build and optimize data engineering workloads on the Lakehouseplatform.Write and optimize complex SQL queries.Solid hands-on experience on dbt with databricks, build modular, testable dbt models for data transformationDevelop and maintain data models in staging, intermediate, and mart layers following data warehousing best practicesWorking knowledge of AWS services like S3, Lambda, EC2, IAM, etc.Containerize data services and applications using Docker & EKS.Ensure data quality, observability, and reliability across the data platform.Document pipelines, models, and data dictionaries to maintain platform knowledge. Required Skills & Experience: Core Data Engineering: 3+ years of professional experience in data engineering.Strong understanding of data platform architecture: Lakehouse, Data Warehouse, Data Lake patterns.Hands-on experience with ETL/ELT design patterns including batch processing and stream processing.Familiarity with ingestion patterns: full load, incremental, CDC, event-driven5+ years of hands-on experience as a Database Administrator in production environments. Strong understanding of database internals — storage engines, transactions, isolation levels, locking, MVCC, query planners.Proven experience supporting mission-critical OLTP workloads with high availability requirements.Solid scripting skills in Bash and/or Python for automation. Databricks:Experience building data pipelines on Databricks (Delta Live Tables, Jobs, Notebooks).Proficiency with PySpark or Spark SQL for large-scale data processing.Familiarity with Delta Lake concepts: ACID transactions, time travel, schema evolution.Orchestration & Ingestion:Proficiency with Apache Airflow — authoring, scheduling, and monitoring DAGs.Experience with Airbyte for managing source-to-destination data connectors.MongoDBHands-on experience administering MongoDB (self-managed and/or Atlas).Replica set configuration, sharding, indexing strategies, and aggregation pipeline tuning.Backup, restore, and disaster recovery using mongodump/mongorestore or Ops Manager. SQL & dbt: Strong SQL skills — query optimization, window functions, CTEs, and complex joins.Experience with dbt (data build tool) for transformation, testing, and documentation. ---Model layering: staging → intermediate → marts ---Writing schema tests, source freshness checks, and macros--dbt testsCloud & Infrastructure Practical experience with AWS services (S3, Lambda, IAM, CloudWatch, etc).Nice to have:Experience with Docker & Kubernetes (EKS) for deploying and scaling data services – Experience running Airflow & Airbyte on KubernetesExperience with data quality frameworks (Great Expectations, Soda.Infrastructure as Code experience (Terraform). Exposure to data governance t